Once data has a structure, traceability and history follow
A single document tells you very little. What came from what, and how it all connects — kernalic builds the structure between your data so the flow reveals itself.
Why structure comes first
However much you collect, data stays unusable until you know how the pieces relate.
We define the relationships
What supersedes what, what contains what — we make the relationships between data explicit.
We make it traceable
Once the relationships are defined, any result can be traced back to the source it came from.
History emerges on its own
Follow the versions and derivations, and the history is already there — nobody has to compile it.
Structure compounds
A structure built once is reused across brands and problem areas, and grows denser as data accumulates.
Sister brands
Two sibling brands built by kernalic. Different problems in intellectual property, solved on the same structure.
One philosophy, two brands
IPSnap and SEPPi solve different problems, but both start from the same question: "Where did this document come from, and what does it connect to?"
IPSnap
Reveals, as a structure, how patent documents relate to one another.
SEPPi
Traces where a standard’s versions and a patent’s claims meet.
The kernalic story
kernalic takes its name from the kernel in deep learning. Just as a kernel extracts meaningful structure from raw data, we set out to extract structure from scattered data. We are starting with two sibling brands, IPSnap and SEPPi.
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We welcome inquiries about product collaboration, partnerships, and hiring.